Per a May 22 press release published via PR Newswire, DICK'S Sporting Goods announced Coach by DICK'S, described as an "agentic AI" conversational adviser that provides tailored product recommendations and training guidance. PR Newswire says the experience is built using Adobe Brand Concierge together with DICK'S proprietary content and sport expertise. Retail Dive reports the retailer will introduce the tool within its mobile app in June and add features over time. PYMNTS notes Coach aims to guide athletes across skill levels and complements prior AI investments such as inventory and RFID automation. PR Newswire includes quotes from Emily Silver, Chief Marketing, eCommerce and Athlete Experience Officer, and Vlad Rak, Chief Technology Officer, on scaling the retailer's expertise through the app.
What happened
Per a May 22 press release published on PR Newswire, DICK'S Sporting Goods introduced Coach by DICK'S, an "agentic AI" conversational experience designed to deliver product recommendations and training "Pro Tips" tailored to an athlete's sport, proficiency level, goals and stated preferences. The release states Coach was built using Adobe Brand Concierge combined with DICK'S content and expertise.
Retail Dive reports the company will introduce the tool within its mobile app in June and add more capabilities over time. PYMNTS' coverage notes Coach is intended to support athletes across stages, from beginners to more advanced shoppers, and that DICK'S has previously invested in AI-driven inventory management and RFID automation per company comments cited by PYMNTS.
PR Newswire includes statements from Emily Silver, Chief Marketing, eCommerce and Athlete Experience Officer, and a quote from Vlad Rak, Chief Technology Officer: "We designed Coach by DICK'S to meet athletes where they are on their sports journey," Rak said in the release.
Editorial analysis - technical context
Agentic conversational interfaces like the one described combine a vendor-provided model or orchestration layer with proprietary content to produce personalized interactions. Industry deployments commonly pair a supplier-managed capability (here Adobe Brand Concierge) with retailer-owned product catalogs, training content, and behavioral signals to personalize recommendations while retaining control over product information and brand voice. For practitioners, those integrations typically require API-level synchronization of inventory, product metadata, and in-app user signals plus governance for content accuracy, personalization logic, and fallback behavior when the model is uncertain.
Industry context
Industry reporting frames this launch as part of a broader trend of retailers embedding AI assistants into commerce flows to increase engagement and conversion. PYMNTS and Retail Dive place Coach within DICK'S multi-year digital investments, including AI-enabled inventory and store automation. Observers at retail conferences have highlighted similar use cases-personalized shopping guidance, conversational search, and after-sale coaching-as incremental ways for legacy retailers to extend in-store expertise into digital channels.
What to watch
For practitioners: monitor conversion lift, average order value, return rates, and customer-reported accuracy of product-fit guidance as primary metrics. Also evaluate how the integration manages inventory consistency between recommendations and actual stock, how it surfaces model uncertainty, and what controls exist for safety and compliance when offering training or exercise guidance. Finally, track the roadmap cadence noted in Retail Dive-app rollout in June and iterative capability additions-to assess feature velocity and operational impact.
Reported sources
PR Newswire (press release), Retail Dive (coverage), PYMNTS (coverage).
Scoring Rationale #
This is a notable commercial deployment of conversational AI by a major retailer that combines vendor tooling and proprietary content. It matters for practitioners tracking retail AI integrations and personalization metrics, but it is not a frontier-model or platform-level shift.
Practice with real Ad Tech data
90 SQL & Python problems · 15 industry datasets
[Active Search Campaigns by BudgetEasy](/problems/sql/active-search-campaigns-by-budget)
[High CPC Clicks & Poor Landing PagesMedium](/problems/sql/high-cpc-clicks-poor-landing-page)
[Campaign ROAS by Attribution ModelHard](/problems/sql/campaign-roas-by-attribution-model)
250 free problems · No credit card